





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A							   =							16
__A							   =							32





def 	lowerCAmelCase_					(			__a						)	->						Tuple:







  """simple docstring"""





  return int(x / 2**20						)



class   _SCREAMING_SNAKE_CASE		:







 '''simple docstring'''

 def __enter__(self							:  List[str])			->Optional[int]:



   '''simple docstring'''



   gc.collect()
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
   lowerCamelCase__: Tuple         =torch.cuda.memory_allocated()
   return self







 def __exit__(self							:  List[Any]			,					*UpperCAmelCase_							:  Optional[Any])			->str:



   '''simple docstring'''



   gc.collect()
   torch.cuda.empty_cache()
   lowerCamelCase__: Tuple         =torch.cuda.memory_allocated()
   lowerCamelCase__: Dict         =torch.cuda.max_memory_allocated()
   lowerCamelCase__: Optional[int]         =bamb(self.end - self.begin)
   lowerCamelCase__: List[str]         =bamb(self.peak - self.begin)
   # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 	lowerCAmelCase_					(			__a      ,       __a = 16      ,       __a = "bert-base-cased"      ,       __a = 320      ,       __a = 160      ,       )	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Optional[Any]         =AutoTokenizer.from_pretrained(__a						)
  lowerCamelCase__: Any         =load_dataset(
      "glue"      ,       "mrpc"      ,       split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}						)

  def tokenize_function(__a						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase__: Optional[int]         =tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=__a      ,       max_length=__a						)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  lowerCamelCase__: Any         =datasets.map(
      __a      ,       batched=__a      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       load_from_cache_file=__a						)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  lowerCamelCase__: Optional[Any]         =tokenized_datasets.rename_column("label"      ,       "labels"						)

  def collate_fn(__a						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__a      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt"						)
    return tokenizer.pad(__a      ,       padding="longest"      ,       return_tensors="pt"						)

  # Instantiate dataloaders.
  lowerCamelCase__: List[Any]         =DataLoader(
      tokenized_datasets["train"]      ,       shuffle=__a      ,       collate_fn=__a      ,       batch_size=__a						)
  lowerCamelCase__: Dict         =DataLoader(
      tokenized_datasets["validation"]      ,       shuffle=__a      ,       collate_fn=__a      ,       batch_size=__a						)

  return train_dataloader, eval_dataloader





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						int:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase__: List[Any]         =config["lr"]
  lowerCamelCase__: List[Any]         =int(config["num_epochs"]						)
  lowerCamelCase__: List[str]         =int(config["seed"]						)
  lowerCamelCase__: Tuple         =int(config["batch_size"]						)
  lowerCamelCase__: Union[str, Any]         =args.model_name_or_path

  set_seed(__a						)
  lowerCamelCase__    ,   lowerCamelCase__: List[str]         =get_dataloaders(__a      ,       __a      ,       __a      ,       args.n_train      ,       args.n_val						)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase__: Union[str, Any]         =AutoModelForSequenceClassification.from_pretrained(__a      ,       return_dict=__a						)

  # Instantiate optimizer
  lowerCamelCase__: List[str]         =(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  lowerCamelCase__: Optional[int]         =optimizer_cls(params=model.parameters()      ,       lr=__a						)

  if accelerator.state.deepspeed_plugin is not None:
    lowerCamelCase__: int         =accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    lowerCamelCase__: Dict         =1
  lowerCamelCase__: Optional[int]         =(len(__a						) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    lowerCamelCase__: int         =get_linear_schedule_with_warmup(
        optimizer=__a      ,       num_warmup_steps=0      ,       num_training_steps=__a      ,       )
  else:
    lowerCamelCase__: Optional[int]         =DummyScheduler(__a      ,       total_num_steps=__a      ,       warmup_num_steps=0						)

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__    ,   lowerCamelCase__: Tuple         =accelerator.prepare(
      __a      ,       __a      ,       __a      ,       __a      ,       __a						)

  # We need to keep track of how many total steps we have iterated over
  lowerCamelCase__: Union[str, Any]         =0
  # We also need to keep track of the stating epoch so files are named properly
  lowerCamelCase__: Union[str, Any]         =0

  # Now we train the model
  lowerCamelCase__: Tuple         ={}
  for epoch in range(__a      ,       __a						):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(__a						):
        lowerCamelCase__: List[str]         =model(**__a						)
        lowerCamelCase__: Optional[Any]         =outputs.loss
        lowerCamelCase__: Dict         =loss / gradient_accumulation_steps
        accelerator.backward(__a						)
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin						)						)						)
    accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used						)						)
    accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked						)						)
    accelerator.print(
        "Total Peak Memory consumed during the train (max): {}".format(
            tracemalloc.peaked + bamb(tracemalloc.begin						)						)						)
    lowerCamelCase__: str         =tracemalloc.peaked + bamb(tracemalloc.begin						)
    if args.peak_memory_upper_bound is not None:
      assert (
          train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
      ), "Peak memory usage exceeded the upper bound"

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir      ,       "peak_memory_utilization.json"						)      ,       "w"						) as f:
      json.dump(__a      ,       __a						)





def 	lowerCAmelCase_					(			)	->						Optional[int]:







  """simple docstring"""





  lowerCamelCase__: Union[str, Any]         =argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
  parser.add_argument(
      "--model_name_or_path"      ,       type=__a      ,       default="bert-base-cased"      ,       help="Path to pretrained model or model identifier from huggingface.co/models."      ,       required=__a      ,       )
  parser.add_argument(
      "--output_dir"      ,       type=__a      ,       default="."      ,       help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."      ,       )
  parser.add_argument(
      "--peak_memory_upper_bound"      ,       type=__a      ,       default=__a      ,       help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."      ,       )
  parser.add_argument(
      "--n_train"      ,       type=__a      ,       default=320      ,       help="Number of training examples to use."      ,       )
  parser.add_argument(
      "--n_val"      ,       type=__a      ,       default=160      ,       help="Number of validation examples to use."      ,       )
  parser.add_argument(
      "--num_epochs"      ,       type=__a      ,       default=1      ,       help="Number of train epochs."      ,       )
  lowerCamelCase__: Optional[int]         =parser.parse_args()
  lowerCamelCase__: Any         ={"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(__a      ,       __a						)


if __name__ == "__main__":
     main()







'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						).convert('RGB'						)

   return image







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # fmt: off

   # vision encoder
   rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')						)
   rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')						)
   rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')						)
   rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")						)

   # QFormer
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')						)
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

   # fmt: on
   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

      # next, set bias in the state dict
      A__       =     torch.cat((q_bias, torch.zeros_like(UpperCAmelCase      ,requires_grad=UpperCAmelCase						), v_bias)						)
      A__       =     qkv_bias







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     364 if 'coco' in model_name else 224
   A__       =     InstructBlipVisionConfig(image_size=UpperCAmelCase						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xxl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,vocab_size=32001						).to_dict()
   elif "vicuna-13b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'      ,vocab_size=32001						).to_dict()
   else:
      raise ValueError('Model name not supported'						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   A__       =     InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
   A__       =     InstructBlipConfig(vision_config=UpperCAmelCase      ,text_config=UpperCAmelCase      ,qformer_config=UpperCAmelCase						)

   return config, image_size







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-uncased'      ,truncation_side='left'						)
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

   if "t5" in model_name:
      A__       =     TaTokenizerFast.from_pretrained('google/flan-t5-xl'      ,truncation_side='left'						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      A__       =     LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'      ,truncation_side='left'      ,bos_token='</s>'      ,unk_token='</s>'						)
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

   A__  ,			A__       =     get_blipa_config(UpperCAmelCase						)
   A__       =     InstructBlipForConditionalGeneration(UpperCAmelCase						).eval()

   A__       =     {
       'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
       'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
       'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
       'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
   }

   A__  ,			A__       =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'						)
   A__       =     'cuda:1' if torch.cuda.is_available() else 'cpu'
   A__       =     'cuda:2' if torch.cuda.is_available() else 'cpu'
   A__  ,			A__  ,			A__       =     load_model_and_preprocess(
       name=UpperCAmelCase      ,model_type=UpperCAmelCase      ,is_eval=UpperCAmelCase      ,device=UpperCAmelCase						)
   original_model.eval()
   print('Done!'						)

   # update state dict keys
   A__       =     original_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      A__       =     state_dict.pop(UpperCAmelCase						)
      if key.startswith('Qformer.bert'						):
         A__       =     key.replace('Qformer.bert'      ,'qformer'						)
      if "attention.self" in key:
         A__       =     key.replace('self'      ,'attention'						)
      if "llm_proj" in key:
         A__       =     key.replace('llm_proj'      ,'language_projection'						)
      if "t5_proj" in key:
         A__       =     key.replace('t5_proj'      ,'language_projection'						)
      if key.startswith('llm_model'						):
         A__       =     key.replace('llm_model'      ,'language_model'						)
      if key.startswith('t5'						):
         A__       =     key.replace('t5'      ,'language'						)
      A__       =     val

   # read in qv biases
   read_in_q_v_bias(UpperCAmelCase      ,UpperCAmelCase						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(UpperCAmelCase      ,strict=UpperCAmelCase						)

   A__       =     load_demo_image()
   A__       =     'What is unusual about this image?'

   # create processor
   A__       =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}      ,image_mean=UpperCAmelCase      ,image_std=UpperCAmelCase						)
   A__       =     InstructBlipProcessor(
       image_processor=UpperCAmelCase      ,tokenizer=UpperCAmelCase      ,qformer_tokenizer=UpperCAmelCase      ,)
   A__       =     processor(images=UpperCAmelCase      ,text=UpperCAmelCase      ,return_tensors='pt'						).to(UpperCAmelCase						)

   # make sure processor creates exact same pixel values
   A__       =     vis_processors['eval'](UpperCAmelCase						).unsqueeze(0						).to(UpperCAmelCase						)
   A__       =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,UpperCAmelCase						)

   original_model.to(UpperCAmelCase						)
   hf_model.to(UpperCAmelCase						)
   with torch.no_grad():
      if "vicuna" in model_name:
         A__       =     original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
         A__       =     hf_model(**UpperCAmelCase						).logits
      else:
         A__       =     original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
         A__       =     tokenizer('\n'      ,return_tensors='pt'						).input_ids.to(UpperCAmelCase						)
         A__       =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,-100						)
         A__       =     hf_model(**UpperCAmelCase      ,labels=UpperCAmelCase						).logits

   print('First values of original logits:'      ,original_logits[0, :3, :3]						)
   print('First values of HF logits:'      ,logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   A__       =     1e-4 if 'vicuna' in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device						)      ,UpperCAmelCase      ,atol=UpperCAmelCase						)
   print('Looks ok!'						)

   print('Generating with original model...'						)
   A__       =     original_model.generate({'image': original_pixel_values, 'prompt': prompt}      ,num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('Generating with HF model...'						)
   A__       =     hf_model.generate(
       **UpperCAmelCase      ,do_sample=UpperCAmelCase      ,num_beams=5      ,max_length=256      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.5      ,length_penalty=1.0      ,temperature=1      ,)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      A__       =     2
   print('Original generation:'      ,UpperCAmelCase						)
   A__       =     processor.batch_decode(UpperCAmelCase      ,skip_special_tokens=UpperCAmelCase						)
   A__       =     [text.strip() for text in output_text]
   print('HF generation:'      ,UpperCAmelCase						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""						)
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    lowerCAmelCase_     						=				[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    lowerCAmelCase_     						=				parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


